Sampling error

Eg: can be caused due to random variations in the estimate.

-         The expected value of such variation = zero

-         In the homogeneous population there will be lesser chance for random variation.

o   So sampling error = smaller magnitude.

-         As sample size = sampling error /////

-         But larger sample result in more systematic bias and it leads more expenditure in data collection.

-         If sampling = complex greater chance = error

It good sample will save time / money / human rexureey

            Sampling can be classified into 3:

1 probably sampling

 2 quasi probably sampling

3 non-probably sampling


probably sampling

in this method each unit of the population has an equal chance of selection.

            This = 3 type

1 simple random sampling

 2 stratified random sampling

3 cluster sampling

1 simple random sampling.

In simple random every element of the population has equal chande of selection in the sampl.

It is used when complete and accurate sampling is available 1 lottery method 2 random number taste 3 computer program method etc. can be used to draw sample randomly.

There are used to avoid bias.

Eg: of /////


 2 stratified random sampling

In this population divided in to “strata” and then random sample is used from each strata.

Eg: people visiting in mall then can be categorised in to different strata on the basis of age / gender / companionate, then random sample can be chosen from each strata.


 3 cluster sampling

The clusters will be formed from the population and then a cluster is chosen randomly.

            Difference between strata & cluster =

Strata: homogeneous (dosha quality ulla ) group of population.

Cluster: heterogeneous                            groups.

Sample is chosen from each strata.

When a cluster is selected all it’s unit are selected.


Quasi probability sampling

2 types:

1 systematic sampling

 2 multistage sampling


systematic sampling:

            1st person chosen randomly then subsequent person chosen by next N th unit of the position.

                        4th / 14th / 24th / 34th….etc.

Multi-stage sampling

            The sample is chosen in multi stage.

Eg: there are total 190 college in 19 districts of Punjab.

Such: each district = 10 colleges.

1000 student study in each college.

A study is conducted which requires collection of data from total 1000 students of 10 college & 5 district.

So researcher choose 5/19 & 2college/district = 5*2=10

1000/10 student 100 students from each 10 colleges.


Non-probability sampling

Selection is not random in such sampling.

Quota sampling:

It is similar like stratified random sampling.

In quota sampling the population is divided into groups then each group is allocated. Quota of same proportion has it in the total population.

Suppose: in the population of 500 researchers

 50% science

40% social science

10% language

So select sample 100/500

                        in 50% researchers from science

                        40% social science

                        10% language

Purposive sampling (judgement sampling)

Sample is selected as per the judgement of the researchers.

Eg: selecting person who can provide information about research.

So those unit of population are selected who meets the purpose of study.

It used when population = small.

Such sample may not be representative sample.

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